Industrial-grade implementation of seq2seq algorithm based on Pytorch, integrated beam search algorithm.
seq2seq is based on other excellent open source projects, this project has the following highlights:
- easy to train, predict and deploy;
- lightweight implementation;
- multitasking support (including dialogue generation and machine translation).
- Encoder: Bidirectional GRU
- Decoder: GRU with Attention Mechanism
- Bahdanau Attention: Neural Machine Translation by Jointly Learning to Align and Translate
- Luong Attention: Effective Approaches to Attention-based Neural Machine Translation
- Diversity Promoting Beam Search: A Simple, Fast Diverse Decoding Algorithm for Neural Generation
seq2seq is dependent on PyTorch. Two ways to install:
Install seq2seq from Pypi:
pip install seq2seq-pytorch
Install seq2seq from the Github source:
git clone https://github.com/Chiang97912/seq2seq.git
cd seq2seq
python setup.py install
from seq2seq.model import Seq2Seq
sources = ['...']
targets = ['...']
model = Seq2Seq('seq2seq-model', embed_size=256, hidden_size=512, lang4src='en', lang4tgt='en', device='cuda:0')
model.fit(sources, targets, epochs=20, batch_size=64)from seq2seq.model import Seq2Seq
model = Seq2Seq('seq2seq-model')
outputs = model.generate('...', beam_size=3, method='greedy')
print(outputs)pythonversion 3.6pyTorchversion 1.9.0torchtextversion 0.3.1numpyversion 1.19.5nltkversion 3.5jiebaversion 0.42.1